Genetic Algorithms and Dynamic Programming for High-Dimensional Optimization

This study develops an advanced dynamic optimization model that leverages Mixed-Integer Linear Programming (MILP) alongside the Genetic Algorithm (GA) to tackle complex resource allocation and configuration challenges under a multitude of constraints. By incorporating uncertain variables and system constraints, the model adeptly handles multidimensional data, employing intelligent optimization algorithms to enhance its precision and robustness. It uniquely considers the interdependencies among parameters, such as complementarity and substitutability effects, ensuring a more accurate representation of real-world dynamics. To validate its efficacy, sensitivity analysis is conducted, showcasing the model's stability and adaptability across various scenarios. The findings underscore the potential of combining MILP and GA in addressing high-dimensional complex systems, providing a solid scientific foundation for future applications and refinements in optimization modeling techniques. This research not only advances the theoretical understanding but also offers practical insights into optimizing resource management in diverse fields.